SoroushVahidi/scidocs huggingface dataset:
Congestion Control in Linux TCP
(2002)

The TCP protocol is used by the majority of the network applications on the Internet. TCP performance is strongly influenced by its congestion control algorithms that limit the amount of transmitted traffic based on the estimated network capacity and utilization. Because the freely available Linux operating system has gained popularity especially in the network servers, its TCP implementation affects many of the network interactions carried out today. We describe the fundamentals of the Linux TCP design, concentrating on the congestion control algorithms. The Linux TCP implementation supports SACK, TCP timestamps, Explicit Congestion Notification, and techniques to undo congestion window adjustments after incorrect congestion notifications. In addition to features specified by IETF, Linux has implementation details beyond the specifications aimed to further improve its performance. We discuss these, and finally show the performance effects of Quick acknowledgements, Rate-halving, and the algorithms for correcting incorrect congestion window adjustments by comparing the performance of Linux TCP implementing these features to the performance achieved with an implementation that does not use the algorithms in question.
A Review of Smart Homes—Past, Present, and Future
(2012)

A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home.
Correction of deep bite in adults using the Invisalign system.
(2008)

719 D overbite is commonly treated by molar extrusion, incisor intrusion, or both. Careful diagnostic assessment is needed to determine the best treatment approach. Patients with structural deep bites may require orthognathic surgery for full correction. The amount of incisor exposure in smiling is an important consideration in deep-bite cases. When the maxillary incisor display is correct, maxillary incisor intrusion is unnecessary; treatment should involve intrusion and leveling of the mandibular anterior teeth to avoid flattening of the smile arc. Any maxillary incisor intrusion or mandibular leveling should be carefully monitored during treatment to maintain proper incisor exposure while effectively correcting the deep bite. After the development of thermoformed appliances that could be used for minor tooth movements, the Invisalign* system was introduced for treatment of moderate malocclusions. In 2004, Wheeler reported that Invisalign could be used in patients with anterior and posterior crossbites, deep bites, anterior open bites or shallow overbites, and periodontal problems. The present article describes the use of Invisalign appliances for the correction of deep bite in adult patients with normal skeletal patterns. Case 1
RFID Application in Hospitals: A Case Study on a Demonstration RFID Project in a Taiwan Hospital
(2006)

After manufacturing and retail marketing, healthcare is considered the next home for Radio Frequency Identification (RFID). Although in its infancy, RFID technology has great potential in healthcare to significantly reduce cost, and improve patient safety and medical services. However, the challenge will be to incorporate RFID into medical practice, especially when relevant experience is limited. To explore this issue, we conducted a case study that demonstrated RFID integration into the medical world at one Taiwan hospital. The project that was studied, the Location-based Medicare Service project, was partially subsidized by the Taiwan government and was aimed at containing SARS, a dangerous disease that struck Taiwan in 2003. Innovative and productive of several significant results, the project established an infrastructure and platform allowing for other applications. In this paper we describe the development strategy, design, and implementation of the project. We discuss our findings pertaining to the collaborative development strategy, device management, data management, and value generation. These findings have important implications for developing RFID applications in healthcare organizations.
Virtualized and flexible ECC for main memory
(2010)

We present a general scheme for virtualizing main memory error-correction mechanisms, which map redundant information needed to correct errors into the memory namespace itself. We rely on this basic idea, which increases flexibility to increase error protection capabilities, improve power efficiency, and reduce system cost; with only small performance overheads. We augment the virtual memory system architecture to detach the physical mapping of data from the physical mapping of its associated ECC information. We then use this mechanism to develop two-tiered error protection techniques that separate the process of detecting errors from the rare need to also correct errors, and thus save energy. We describe how to provide strong chipkill and double-chip kill protection using existing DRAM and packaging technology. We show how to maintain access granularity and redundancy overheads, even when using ×8 DRAM chips. We also evaluate error correction for systems that do not use ECC DIMMs. Overall, analysis of demanding SPEC CPU 2006 and PARSEC benchmarks indicates that performance overhead is only 1% with ECC DIMMs and less than 10% using standard Non-ECC DIMM configurations, that DRAM power savings can be as high as 27%, and that the system energy-delay product is improved by 12% on average.
FRIP: a region-based image retrieval tool using automatic image segmentation and stepwise Boolean AND matching
(2005)

We present our region-based image retrieval tool, finding region in the picture (FRIP), that is able to accommodate, to the extent possible, region scaling, rotation, and translation. Our goal is to develop an effective retrieval system to overcome a few limitations associated with existing systems. To do this, we propose adaptive circular filters used for semantic image segmentation, which are based on both Bayes' theorem and texture distribution of image. In addition, to decrease the computational complexity without losing the accuracy of the search results, we extract optimal feature vectors from segmented regions and apply them to our stepwise Boolean AND matching scheme. The experimental results using real world images show that our system can indeed improve retrieval performance compared to other global property-based or region-of-interest-based image retrieval methods.
Today the Earwig, Tomorrow Man?
(1991)

Kirsh, D., Today the earwig, tomorrow man?, Artificial Intelligence 47 (1991) 161-184. A startling amount of intelligent activity can be controlled without reasoning or thought. By tuning the perceptual system to task relevant properties a creature can cope with relatively sophisticated environments without concepts. There is a limit, however, to how far a creature without concepts can go. Rod Brooks, like many ecologically oriented scientists, argues that the vast majority of intelligent behaviour is concept-free. To evaluate this position I consider what special benefits accrue to concept-using creatures. Concepts are either necessary for certain types of perception, learning, and control, or they make those processes computationally simpler. Once a creature has concepts its capacities are vastly multiplied.
Preputial flaps to correct buried penis
(2007)

The authors developed a preputial skin flap technique to correct the buried penis which was simple and practical. This simple procedure can be applied to most boys with buried penis. In the last 3 years, we have seen 12 boys with buried penis and have been treated by using preputial flaps. The mean age is about 5.1 (from 3 to 12). By making a longitudinal incision on the ventral side of penis, the tightness of the foreskin is released and leave a diamond-shaped skin defect. It allows the penile shaft to extend out. A circumferential incision is made about 5 mm proximal to the coronal sulcus. Pedicled preputial flaps are obtained leaving optimal penile skin on the dorsal side. The preputial skin flaps are rotated onto the ventral side and tailored to cover the defect. All patients are followed for at least 3 months. Edema and swelling on the flaps are common, but improves with time. None of our patients need a second operation. The preputial flaps technique is a simple technique which allows surgeons to deal with most cases of buried penis by tailoring the flaps providing good cosmetic and functional results.
360ProbDASH: Improving QoE of 360 Video Streaming Using Tile-based HTTP Adaptive Streaming
(2017)

Recently, there has been a significant interest towards 360-degree panorama video. However, such videos usually require extremely high bitrate which hinders their widely spread over the Internet. Tile-based viewport adaptive streaming is a promising way to deliver 360-degree video due to its on-request portion downloading. But it is not trivial for it to achieve good Quality of Experience (QoE) because Internet request-reply delay is usually much higher than motion-to-photon latency. In this paper, we leverage a probabilistic approach to pre-fetch tiles countering viewport prediction error, and design a QoE-driven viewport adaptation system, 360ProbDASH. It treats user's head movement as probability events, and constructs a probabilistic model to depict the distribution of viewport prediction error. A QoE-driven optimization framework is proposed to minimize total expected distortion of pre-fetched tiles. Besides, to smooth border effects of mixed-rate tiles, the spatial quality variance is also minimized. With the requirement of short-term viewport prediction under a small buffer, it applies a target-buffer-based rate adaptation algorithm to ensure continuous playback. We implement 360ProbDASH prototype and carry out extensive experiments on a simulation test-bed and real-world Internet with real user's head movement traces. The experimental results demonstrate that 360ProbDASH achieves at almost 39% gains on viewport PSNR, and 46% reduction on spatial quality variance against the existed viewport adaptation methods.
Hierarchical Convolutional Features for Visual Tracking
(2015)

Visual object tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we exploit features extracted from deep convolutional neural networks trained on object recognition datasets to improve tracking accuracy and robustness. The outputs of the last convolutional layers encode the semantic information of targets and such representations are robust to significant appearance variations. However, their spatial resolution is too coarse to precisely localize targets. In contrast, earlier convolutional layers provide more precise localization but are less invariant to appearance changes. We interpret the hierarchies of convolutional layers as a nonlinear counterpart of an image pyramid representation and exploit these multiple levels of abstraction for visual tracking. Specifically, we adaptively learn correlation filters on each convolutional layer to encode the target appearance. We hierarchically infer the maximum response of each layer to locate targets. Extensive experimental results on a largescale benchmark dataset show that the proposed algorithm performs favorably against state-of-the-art methods.
Assessing Test Suite E ↵ ectiveness Using Static Metrics
(2018)

With the increasing amount of automated tests, we need ways to measure the test e↵ectiveness. The state-of-the-art technique for assessing test e↵ectiveness, mutation testing, is too slow and cumbersome to be used in large scale evolution studies or code audits by external companies. In this paper we investigated two alternatives, namely code coverage and assertion count. We discovered that code coverage outperforms assertion count by showing a relation with test suite e↵ectiveness for all analysed project. Assertion count only displays such a relation in only one of the analysed projects. Further analysing this relationship between assertion count coverage and test e↵ectiveness would allow to circumvent some of the problems of mutation testing.
Universal Stanford dependencies: A cross-linguistic typology
(2014)

Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones. We suggest a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added. We emphasize the lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology. We show how existing dependency schemes for several languages map onto the universal taxonomy proposed here and close with consideration of practical implications of dependency representation choices for NLP applications, in particular parsing.
Computational modelling of visual attention
(2001)

Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.
On-line Boosting and Vision
(2006)

Boosting has become very popular in computer vision, showing impressive performance in detection and recognition tasks. Mainly off-line training methods have been used, which implies that all training data has to be a priori given; training and usage of the classifier are separate steps. Training the classifier on-line and incrementally as new data becomes available has several advantages and opens new areas of application for boosting in computer vision. In this paper we propose a novel on-line AdaBoost feature selection method. In conjunction with efficient feature extraction methods the method is real time capable. We demonstrate the multifariousness of the method on such diverse tasks as learning complex background models, visual tracking and object detection. All approaches benefit significantly by the on-line training.
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
(2008)

Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations.
Learning to associate: HybridBoosted multi-target tracker for crowded scene
(2009)

We propose a learning-based hierarchical approach of multi-target tracking from a single camera by progressively associating detection responses into longer and longer track fragments (tracklets) and finally the desired target trajectories. To define tracklet affinity for association, most previous work relies on heuristically selected parametric models; while our approach is able to automatically select among various features and corresponding non-parametric models, and combine them to maximize the discriminative power on training data by virtue of a HybridBoost algorithm. A hybrid loss function is used in this algorithm because the association of tracklet is formulated as a joint problem of ranking and classification: the ranking part aims to rank correct tracklet associations higher than other alternatives; the classification part is responsible to reject wrong associations when no further association should be done. Experiments are carried out by tracking pedestrians in challenging datasets. We compare our approach with state-of-the-art algorithms to show its improvement in terms of tracking accuracy.
Novel approach to nonlinear / non-Gaussian Bayesian state estimation
(2004)

An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.
The Boosting Approach to Machine Learning: An Overview
(2003)

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting.
Air Traffic Security : Aircraft Classification Using ADS-B Message ’ s Phase-Pattern
(2017)

Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance system used in Air Traffic Control. With this system, the aircraft transmits their own information (identity, position, velocity, etc.) to any equipped listener for surveillance scope. The ADS-B is based on a very simple protocol and does not provide any kind of authentication and encryption, making it vulnerable to many types of cyber-attacks. In the paper, the use of the airplane/transmitter carrier phase is proposed as a feature to perform a classification of the aircraft and, therefore, distinguish legitimate messages from fake ones. The feature extraction process is described and a classification method is selected. Finally, a complete intruder detection algorithm is proposed and evaluated with real data.
WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming
(2018)

The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. In this paper, we present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.